Fall detection with accelerometer data using Residual Networks adapted to multi-variate time series classification

In elderly healthcare the risk of falling is elevated, and falling can have serious consequences. Early fall detection can greatly improve patient outcomes. Sensors including accelerometers can provide the data necessary for fall detection, but correctly distinguishing falls from non-falls is a difficult task. In this paper we survey recent advances including the use of recurrent and convolutional neural networks, and we propose a novel ResNet architecture and variants, adapted to multivariate time series data. We carry out a systematic experiment comparing our method against several baselines and recent state-of-the-art models, using the Farseeing dataset. We find that the proposed ResNet architecture is the best-performing model across metrics, and we investigate the internal features it learns to enable this. We also investigate different approaches to data augmentation in the context of the lack of labelled data.